Mapping targets to single cells in plaques.
Accompanying ‘Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.’
Sander W. van der Laan, PhD | @swvanderlaan | s.w.vanderlaan@gmail.com
2022-03-19
General Setup
Create a new analysis directory...
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[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
[1] "_archived" "1. AEDB.CEA.baseline.nb.html" "1. AEDB.CEA.baseline.Rmd"
[4] "2. SNP_analyses.Rmd" "20220319.HDAC9.AEDB.CEA.baseline.RData" "20220319.HDAC9.bulkRNAseq.main_analysis.RData"
[7] "20220319.HDAC9.bulkRNAseq.preparation.RData" "3.1 bulkRNAseq.preparation.nb.html" "3.1 bulkRNAseq.preparation.Rmd"
[10] "3.2 bulkRNAseq.main_analysis.nb.html" "3.2 bulkRNAseq.main_analysis.Rmd" "3.3 bulkRNAseq.additional_figures.Rmd"
[13] "4. scRNAseq.Rmd" "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj" "AnalysisPlan"
[16] "HDAC9" "images" "LICENSE"
[19] "README.html" "README.md" "references.bib"
[22] "renv" "renv.lock" "scripts"
[25] "SNP" "targets" source(paste0(PROJECT_loc, "/scripts/functions.R"))ggplot2::theme_set(ggplot2::theme_minimal())
pander::panderOptions("table.split.table", Inf)install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")
# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)
install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")
install.packages.auto("openxlsx")
install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")
install.packages.auto("BlandAltmanLeh")
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")
install.packages.auto("ggpubr")
install.packages.auto("UpSetR")
devtools::install_github("thomasp85/patchwork")Using github PAT from envvar GITHUB_PAT
Skipping install of 'patchwork' from a github remote, the SHA1 (79223d30) has not changed since last install.
Use `force = TRUE` to force installation# for Seurat etc
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
# install.packages("Seurat")
install.packages.auto("Seurat") # latest versionLoading required package: Seurat
Attaching SeuratObject
Attaching package: 'Seurat'
The following object is masked from 'package:Hmisc':
Key
The following object is masked from 'package:SummarizedExperiment':
Assays
The following object is masked from 'package:DT':
JSlibrary("Seurat")
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color HEX (RGB) CHR MAF/INFO
###---------------------------------------------------------------------------------------
### 1 yellow #FBB820 (251,184,32) => 1 or 1.0>INFO
### 2 gold #F59D10 (245,157,16) => 2
### 3 salmon #E55738 (229,87,56) => 3 or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4 darkpink #DB003F ((219,0,63) => 4
### 5 lightpink #E35493 (227,84,147) => 5 or 0.8<INFO<1.0
### 6 pink #D5267B (213,38,123) => 6
### 7 hardpink #CC0071 (204,0,113) => 7
### 8 lightpurple #A8448A (168,68,138) => 8
### 9 purple #9A3480 (154,52,128) => 9
### 10 lavendel #8D5B9A (141,91,154) => 10
### 11 bluepurple #705296 (112,82,150) => 11
### 12 purpleblue #686AA9 (104,106,169) => 12
### 13 lightpurpleblue #6173AD (97,115,173/101,120,180) => 13
### 14 seablue #4C81BF (76,129,191) => 14
### 15 skyblue #2F8BC9 (47,139,201) => 15
### 16 azurblue #1290D9 (18,144,217) => 16 or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17 lightazurblue #1396D8 (19,150,216) => 17
### 18 greenblue #15A6C1 (21,166,193) => 18
### 19 seaweedgreen #5EB17F (94,177,127) => 19
### 20 yellowgreen #86B833 (134,184,51) => 20
### 21 lightmossgreen #C5D220 (197,210,32) => 21
### 22 mossgreen #9FC228 (159,194,40) => 22 or MAF>0.20 or 0.6<INFO<0.8
### 23 lightgreen #78B113 (120,177,19) => 23/X
### 24 green #49A01D (73,160,29) => 24/Y
### 25 grey #595A5C (89,90,92) => 25/XY or MAF<0.01 or 0.0<INFO<0.2
### 26 lightgrey #A2A3A4 (162,163,164) => 26/MT
###
### ADDITIONAL COLORS
### 27 midgrey #D7D8D7
### 28 verylightgrey #ECECEC"
### 29 white #FFFFFF
### 30 black #000000
###----------------------------------------------------------------------------------------------
uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
"#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
"#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
"#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
"#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
"#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
"#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
"#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
"#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
"#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------ERA-CVD ‘druggable-MI-targets’
For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:
conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Saturday, March 19, 2022 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.
single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Saturday, March 19, 2022 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.
Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.
Background
Here we map the HDAC9 to single-cells from the plaques.
library(openxlsx)
gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")
target_genes <- unlist(gene_list_df$Gene)
target_genes[1] "HDAC9" "TWIST1" "IL6" "IL1B" Load data
First we will load the data:
- scRNAseq experimental data and rename the cell types.
- Athero-Express clinical data.
Here we load the latest dataset from our Athero-Express single-cell RNA experiment.
# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
#
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqDataAn object of class Seurat
36147 features across 4948 samples within 2 assays
Active assay: RNA (20111 features, 0 variable features)
1 other assay present: SCT
2 dimensional reductions calculated: pca, umapThe naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells”-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exhibit similar properties, i.e. similar defining markers (e.g. KIT).
We will rename the cell types to human readable names.
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")
unique(scRNAseqData@active.ident) [1] CD3+ T Cells I CD3+ T Cells IV CD34+ Endothelial Cells I
[4] CD3+ T Cells V CD3+CD56+ NK Cells II CD3+ T Cells VI
[7] CD68+IL18+TLR4+TREM2+ Resident macrophages CD3+CD56+ NK Cells I ACTA2+ Smooth Muscle Cells
[10] CD3+ T Cells II FOXP3+ T Cells CD34+ Endothelial Cells II
[13] CD3+ T Cells III CD68+CD1C+ Dendritic Cells CD68+CASP1+IL1B+SELL+ Inflammatory macrophages
[16] CD79A+ Class-switched Memory B Cells CD68+ABCA1+OLR1+TREM2+ Foam Cells CD68+KIT+ Mast Cells
[19] CD68+CD4+ Monocytes CD79+ Plasma B Cells
20 Levels: CD3+ T Cells I CD3+ T Cells II CD3+ T Cells III CD3+ T Cells IV CD68+IL18+TLR4+TREM2+ Resident macrophages ... CD79+ Plasma B Cellscelltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes",
"CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
"CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
"CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
# T-cells
"CD3+ T Cells I" = "CD3+ TC I",
"CD3+ T Cells II" = "CD3+ TC II",
"CD3+ T Cells III" = "CD3+ TC III",
"CD3+ T Cells IV" = "CD3+ TC IV",
"CD3+ T Cells V" = "CD3+ TC V",
"CD3+ T Cells VI" = "CD3+ TC VI",
"FOXP3+ T Cells" = "FOXP3+ TC",
# Endothelial cells
"CD34+ Endothelial Cells I" = "CD34+ EC I",
"CD34+ Endothelial Cells II" = "CD34+ EC II",
# SMC
"ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC",
# NK Cells
"CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
"CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
# Mast
"CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
"CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem",
"CD79+ Plasma B Cells" = "CD79+ BCplasma")
scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData,
celltypes)UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
Clinical data
Loading the Athero-Express clinical data.
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220319.",TRAIT_OF_INTEREST,".AEDB.CEA.RDS"))
# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
# "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
# "hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
"MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
"Neutrophils_rankNorm", "MastCells_rankNorm",
"IPH.bin", "VesselDensity_rankNorm",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
basetable_bin = c("Gender", "Artery_summary",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAESCRNA: baseline characteristics
Preparation
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)
scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)[1] 46 1144# Replace missing data
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)
na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A",
"Not Available", "Not available",
"missing",
"-999", "-99",
"No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.
scRNAseqDataMetaAE %>%
replace_with_na_all(condition = ~.x %in% na_strings)cat("====================================================================================================")====================================================================================================cat("SELECTION THE SHIZZLE")SELECTION THE SHIZZLEcat("- sanity checking PRIOR to selection")- sanity checking PRIOR to selectionlibrary(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany") Hospital
Sex St. Antonius, Nieuwegein UMC Utrecht <NA>
female 0 10 0
male 0 26 0
<NA> 0 0 10ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany") Artery
Sex female male <NA>
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0 0
carotid (left & right) 10 25 0
femoral/iliac (left, right or both sides) 0 0 0
other carotid arteries (common, external) 0 1 0
carotid bypass and injury (left, right or both sides) 0 0 0
aneurysmata (carotid & femoral) 0 0 0
aorta 0 0 0
other arteries (renal, popliteal, vertebral) 0 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0 0
<NA> 0 0 10ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany") ae.gender
ae.ic female male <NA>
missing 0 0 0
no, died 0 0 0
yes 6 14 0
yes, health treatment when possible 2 7 0
yes, no health treatment 1 2 0
yes, no health treatment, no commercial business 1 2 0
yes, no tissue, no commerical business 0 0 0
yes, no tissue, no questionnaires, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no questionnaires, health treatment when possible 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commerical business 0 0 0
yes, no health treatment, no medical info, no commercial business 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment 0 0 0
yes, no tissue, no health treatment 0 0 0
yes, no tissue, no questionnaires 0 0 0
yes, no tissue, health treatment when possible 0 0 0
yes, no tissue 0 0 0
yes, no commerical business 0 1 0
yes, health treatment when possible, no commercial business 0 0 0
yes, no medical info, no commercial business 0 0 0
yes, no questionnaires 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no medical info 0 0 0
yes, no questionnaires, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no medical info 0 0 0
yes, no questionnaires, health treatment when possible, no commercial business 0 0 0
yes, no health treatment, no medical info 0 0 0
no, doesn't want to 0 0 0
no, unable to sign 0 0 0
no, no reaction 0 0 0
no, lost 0 0 0
no, too old 0 0 0
yes, no medical info, health treatment when possible 0 0 0
no (never asked for IC because there was no tissue) 0 0 0
yes, no medical info, no commercial business, health treatment when possible 0 0 0
no, endpoint 0 0 0
wil niets invullen, wel alles gebruiken 0 0 0
second informed concents: yes, no commercial business 0 0 0
nooit geincludeerd 0 0 0
<NA> 0 0 10rm(ae.gender, ae.hospital, ae.artery, ae.ic)
scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd" &
informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no commerical business" &
informedconsent != "yes, health treatment when possible, no commercial business" &
informedconsent != "yes, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no questionnaires, no commercial business" &
informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" &
informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)[1] 32 1144# DT::datatable(scRNAseqDataMetaAE.all)Baseline
Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.
cat("===========================================================================================")===========================================================================================cat("CREATE BASELINE TABLE")CREATE BASELINE TABLE# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = scRNAseqDataMetaAE.all, includeNA = TRUE),
nonnormal = c(),
quote = FALSE, showAllLevels = TRUE,
format = "p",
contDigits = 3)[,1:2]Warning in ModuleReturnVarsExist(vars, data) :
These variables only have NA/NaN: MAC_rankNorm SMC_rankNorm Macrophages.bin SMC.bin Neutrophils_rankNorm MastCells_rankNorm IPH.bin VesselDensity_rankNorm Calc.bin Collagen.bin Fat.bin_10 Fat.bin_40 OverallPlaquePhenotype Dropped
level Overall
n 32
Hospital (%) St. Antonius, Nieuwegein 0.0
UMC Utrecht 100.0
ORyear (%) No data available/missing 0.0
2002 0.0
2003 0.0
2004 0.0
2005 0.0
2006 0.0
2007 0.0
2008 0.0
2009 0.0
2010 0.0
2011 0.0
2012 0.0
2013 0.0
2014 0.0
2015 0.0
2016 0.0
2017 0.0
2018 62.5
2019 37.5
Artery_summary (%) No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0.0
carotid (left & right) 96.9
femoral/iliac (left, right or both sides) 0.0
other carotid arteries (common, external) 3.1
carotid bypass and injury (left, right or both sides) 0.0
aneurysmata (carotid & femoral) 0.0
aorta 0.0
other arteries (renal, popliteal, vertebral) 0.0
femoral bypass, angioseal and injury (left, right or both sides) 0.0
Age (mean (SD)) 72.156 (8.651)
Gender (%) female 28.1
male 71.9
TC_final (mean (SD)) 4.467 (1.261)
LDL_final (mean (SD)) 2.659 (1.008)
HDL_final (mean (SD)) 1.120 (0.238)
TG_final (mean (SD)) 1.892 (1.124)
systolic (mean (SD)) 152.516 (27.422)
diastoli (mean (SD)) 80.452 (17.171)
GFR_MDRD (mean (SD)) 82.510 (32.197)
BMI (mean (SD)) 26.458 (3.556)
KDOQI (%) No data available/missing 0.0
Normal kidney function 31.2
CKD 2 (Mild) 34.4
CKD 3 (Moderate) 25.0
CKD 4 (Severe) 0.0
CKD 5 (Failure) 0.0
<NA> 9.4
BMI_WHO (%) No data available/missing 0.0
Underweight 3.1
Normal 31.2
Overweight 40.6
Obese 15.6
<NA> 9.4
SmokerStatus (%) Current smoker 34.4
Ex-smoker 46.9
Never smoked 12.5
<NA> 6.2
AlcoholUse (%) No 34.4
Yes 56.2
<NA> 9.4
DiabetesStatus (%) Control (no Diabetes Dx/Med) 65.6
Diabetes 31.2
<NA> 3.1
Hypertension.selfreport (%) No data available/missing 0.0
no 9.4
yes 84.4
<NA> 6.2
Hypertension.selfreportdrug (%) No data available/missing 0.0
no 9.4
yes 84.4
<NA> 6.2
Hypertension.composite (%) No data available/missing 0.0
no 6.2
yes 90.6
<NA> 3.1
Hypertension.drugs (%) No data available/missing 0.0
no 6.2
yes 87.5
<NA> 6.2
Med.anticoagulants (%) No data available/missing 0.0
no 84.4
yes 6.2
<NA> 9.4
Med.all.antiplatelet (%) No data available/missing 0.0
no 25.0
yes 68.8
<NA> 6.2
Med.Statin.LLD (%) No data available/missing 0.0
no 21.9
yes 71.9
<NA> 6.2
Stroke_Dx (%) Missing 0.0
No stroke diagnosed 50.0
Stroke diagnosed 46.9
<NA> 3.1
sympt (%) missing 0.0
Asymptomatic 18.8
TIA 15.6
minor stroke 31.2
Major stroke 6.2
Amaurosis fugax 15.6
Four vessel disease 0.0
Vertebrobasilary TIA 0.0
Retinal infarction 3.1
Symptomatic, but aspecific symtoms 0.0
Contralateral symptomatic occlusion 0.0
retinal infarction 3.1
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0
retinal infarction + TIAs 0.0
Ocular ischemic syndrome 6.2
ischemisch glaucoom 0.0
subclavian steal syndrome 0.0
TGA 0.0
Symptoms.5G (%) Asymptomatic 18.8
Ocular 21.9
Other 0.0
Retinal infarction 6.2
Stroke 37.5
TIA 15.6
AsymptSympt (%) Asymptomatic 18.8
Ocular and others 28.1
Symptomatic 53.1
AsymptSympt2G (%) Asymptomatic 18.8
Symptomatic 81.2
Symptoms.Update2G (%) Asymptomatic 18.8
Symptomatic 81.2
Symptoms.Update3G (%) Asymptomatic 18.8
Symptomatic 81.2
Unclear 0.0
indexsymptoms_latest_4g (mean (SD)) 1.719 (1.170)
restenos (%) missing 0.0
de novo 100.0
restenosis 0.0
stenose bij angioseal na PTCA 0.0
stenose (%) missing 0.0
0-49% 3.1
50-70% 12.5
70-90% 43.8
90-99% 21.9
100% (Occlusion) 0.0
NA 0.0
50-99% 0.0
70-99% 18.8
99 0.0
CAD_history (%) Missing 0.0
No history CAD 78.1
History CAD 18.8
<NA> 3.1
PAOD (%) missing/no data 0.0
no 81.2
yes 15.6
<NA> 3.1
Peripheral.interv (%) no 78.1
yes 18.8
<NA> 3.1
EP_composite (%) No data available. 0.0
No composite endpoints 37.5
Composite endpoints 12.5
<NA> 50.0
EP_composite_time (mean (SD)) 0.905 (0.522)
EP_major (%) No data available. 0.0
No major events (endpoints) 40.6
Major events (endpoints) 9.4
<NA> 50.0
EP_major_time (mean (SD)) 0.905 (0.522)
Plaque_Vulnerability_Index (%) 0 100.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0 Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_commercial.BaselineTable.xlsx"),
# format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) ,
# rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.32pts.after_qc.IC_academic.BaselineTable.xlsx"),
format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) ,
rowNames = TRUE, colNames = TRUE, overwrite = TRUE)AESCRNA
Quality control
Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.
## check stuff
cat("\nHow many cells per type ...?")
How many cells per type ...?sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))integer(0)# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))
# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)
cat("\n\nHow many cells per patient ...?")
How many cells per patient ...?sort(table(scRNAseqData@meta.data$Patient))
4530 4675 4440 4605 4653 4472 4458 4455 4476 4587 4496 4601 4502 4501 4571 4478 4448 4477 4452 4459 4520 4602 4489 4432 4495 4545 4558 4480 4447 4500 4513 4535
3 4 6 7 20 22 35 54 59 60 70 70 73 75 76 77 80 84 92 94 96 96 97 99 102 106 107 112 114 116 123 130
4676 4486 4470 4487 4546 4488 4521 4580 4491 4541 4450 4542 4453 4443
135 137 144 144 144 146 161 163 175 178 205 213 222 422 cat("\n\nVisualizing these ratio's per study number and sample ...?")
Visualizing these ratio's per study number and sample ...?UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())Saving 18 x 12 in imageggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())Saving 18 x 12 in image# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)),
# cex.axis = 1.0, cex.names = 0.5, las = 1,
# col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)),
# cex.axis = 1.0, cex.names = 0.5, las = 2,
# col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()Visualisations
Let’s project known cellular markers.
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols = c("#ECECEC", "#DB003F"))
# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols = c("#ECECEC", "#DB003F"))
# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD36"), cols = c("#ECECEC", "#DB003F"))
# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD4"), cols = c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols = c("#ECECEC", "#DB003F"))
# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols = c("#ECECEC", "#DB003F"))
# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols = c("#ECECEC", "#DB003F"))
# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols = c("#ECECEC", "#DB003F"))
Targets of interest:
We check whether the targets genes were sequenced using our method.
length(target_genes)[1] 4target_genes[1] "HDAC9" "TWIST1" "IL6" "IL1B" Expression in cell communities
# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "RP11-145E5.5",
# "ZNF32", "BCAM", "DUPD1", "PVRL2")
#
# temp = target_genes[!target_genes %in% target_genes_rm]
#
# target_genes_qc <- c(temp, "DUSP27", "NECTIN2")
target_genes_qc <- target_genes
target_genes_qc[1] "HDAC9" "TWIST1" "IL6" "IL1B" library(RColorBrewer)
p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
cols = "RdBu")
p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())Saving 18 x 12 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())Saving 18 x 12 in imageggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())Saving 18 x 12 in imagerm(p1)
# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
# cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
# combine = TRUE)
#
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())# VlnPlot(scRNAseqData, features = "DUSP27")
# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")),
dir.create(file.path(PLOT_loc, "/VlnPlot")),
FALSE)[1] TRUEVlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")
for (GENE in target_genes_qc){
print(paste0("Projecting the expression of ", GENE, "."))
vp1 <- VlnPlot(scRNAseqData, features = GENE) +
xlab("cell communities") +
ylab(bquote("normalized expression")) +
theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
axis.title.y = element_text(color = "#000000", size = 14, face = "bold"),
legend.position = "none")
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
# print(vp1)
}[1] "Projecting the expression of HDAC9."Saving 7 x 7 in image
Saving 7 x 7 in image
Saving 7 x 7 in image[1] "Projecting the expression of TWIST1."Saving 7 x 7 in image
Saving 7 x 7 in image
Saving 7 x 7 in image[1] "Projecting the expression of IL6."Saving 7 x 7 in image
Saving 7 x 7 in image
Saving 7 x 7 in image[1] "Projecting the expression of IL1B."Saving 7 x 7 in image
Saving 7 x 7 in image
Saving 7 x 7 in imageDifferential expression between cell communities
Here we project genes to only the broad cell communities:
- macrophages
- endothelial cells
- smooth muscle cells
- T-cells
- B-cells
- Mast cells
- NK-cells
- Mixed cells
Macrophages
unique(scRNAseqData@active.ident) [1] CD3+ TC I CD3+ TC IV CD34+ EC I CD3+ TC V CD3+CD56+ NK II
[6] CD3+ TC VI CD68+IL18+TLR4+TREM2+ MRes CD3+CD56+ NK I ACTA2+ SMC CD3+ TC II
[11] FOXP3+ TC CD34+ EC II CD3+ TC III CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf
[16] CD79A+ BCmem CD68+ABCA1+OLR1+TREM2+ FC CD68+KIT+ MC CD68+CD4+ Mono CD79+ BCplasma
20 Levels: CD68+CD4+ Mono CD68+IL18+TLR4+TREM2+ MRes CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf CD68+ABCA1+OLR1+TREM2+ FC CD3+ TC I CD3+ TC II ... CD79+ BCplasmaComparison between the macrophages cell communities (CD14/CD68+), and all other communities.
MAC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC"),
ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf",
#"CD68+CD1C+ DC",
#"CD68+CD4+ Mono",
#"CD68+IL18+TLR4+TREM2+ MRes",
#"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 05s
|++ | 2 % ~01m 02s
|++ | 3 % ~01m 02s
|+++ | 4 % ~01m 03s
|+++ | 5 % ~01m 04s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 02s
|+++++ | 8 % ~01m 01s
|+++++ | 9 % ~01m 02s
|++++++ | 10% ~01m 01s
|++++++ | 11% ~60s
|+++++++ | 12% ~59s
|+++++++ | 13% ~58s
|++++++++ | 14% ~58s
|++++++++ | 15% ~57s
|+++++++++ | 16% ~56s
|+++++++++ | 17% ~56s
|++++++++++ | 18% ~55s
|++++++++++ | 19% ~54s
|+++++++++++ | 20% ~53s
|+++++++++++ | 21% ~53s
|++++++++++++ | 22% ~53s
|++++++++++++ | 23% ~52s
|+++++++++++++ | 24% ~51s
|+++++++++++++ | 25% ~50s
|++++++++++++++ | 26% ~50s
|++++++++++++++ | 27% ~49s
|+++++++++++++++ | 28% ~48s
|+++++++++++++++ | 29% ~48s
|++++++++++++++++ | 30% ~47s
|++++++++++++++++ | 31% ~46s
|+++++++++++++++++ | 32% ~46s
|+++++++++++++++++ | 33% ~45s
|++++++++++++++++++ | 34% ~44s
|++++++++++++++++++ | 35% ~44s
|+++++++++++++++++++ | 36% ~43s
|+++++++++++++++++++ | 37% ~42s
|++++++++++++++++++++ | 38% ~41s
|++++++++++++++++++++ | 39% ~41s
|+++++++++++++++++++++ | 40% ~40s
|+++++++++++++++++++++ | 41% ~39s
|++++++++++++++++++++++ | 42% ~39s
|++++++++++++++++++++++ | 43% ~38s
|+++++++++++++++++++++++ | 44% ~37s
|+++++++++++++++++++++++ | 45% ~37s
|++++++++++++++++++++++++ | 46% ~36s
|++++++++++++++++++++++++ | 47% ~35s
|+++++++++++++++++++++++++ | 48% ~35s
|+++++++++++++++++++++++++ | 49% ~34s
|++++++++++++++++++++++++++ | 51% ~34s
|++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 53% ~32s
|+++++++++++++++++++++++++++ | 54% ~31s
|++++++++++++++++++++++++++++ | 55% ~31s
|++++++++++++++++++++++++++++ | 56% ~30s
|+++++++++++++++++++++++++++++ | 57% ~29s
|+++++++++++++++++++++++++++++ | 58% ~29s
|++++++++++++++++++++++++++++++ | 59% ~28s
|++++++++++++++++++++++++++++++ | 60% ~27s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|+++++++++++++++++++++++++++++++ | 62% ~26s
|++++++++++++++++++++++++++++++++ | 63% ~25s
|++++++++++++++++++++++++++++++++ | 64% ~25s
|+++++++++++++++++++++++++++++++++ | 65% ~24s
|+++++++++++++++++++++++++++++++++ | 66% ~23s
|++++++++++++++++++++++++++++++++++ | 67% ~23s
|++++++++++++++++++++++++++++++++++ | 68% ~22s
|+++++++++++++++++++++++++++++++++++ | 69% ~21s
|+++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 71% ~20s
|++++++++++++++++++++++++++++++++++++ | 72% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~18s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 08sDT::datatable(MAC.markers)MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
lab = rownames(MAC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Macrophage markers\n(Macrophage communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"),
plot = MAC_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)
temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Smooth muscle cells
Comparison between the smooth muscle cell communities (ACTA2+), and all other communities.
SMC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("ACTA2+ SMC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
#"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 29s
|++ | 2 % ~01m 30s
|++ | 3 % ~01m 27s
|+++ | 4 % ~01m 25s
|+++ | 5 % ~01m 22s
|++++ | 6 % ~01m 22s
|++++ | 7 % ~01m 20s
|+++++ | 8 % ~01m 22s
|+++++ | 9 % ~01m 20s
|++++++ | 10% ~01m 21s
|++++++ | 11% ~01m 21s
|+++++++ | 12% ~01m 19s
|+++++++ | 13% ~01m 20s
|++++++++ | 14% ~01m 19s
|++++++++ | 15% ~01m 18s
|+++++++++ | 16% ~01m 18s
|+++++++++ | 17% ~01m 18s
|++++++++++ | 18% ~01m 17s
|++++++++++ | 19% ~01m 15s
|+++++++++++ | 20% ~01m 14s
|+++++++++++ | 21% ~01m 13s
|++++++++++++ | 22% ~01m 11s
|++++++++++++ | 23% ~01m 10s
|+++++++++++++ | 24% ~01m 09s
|+++++++++++++ | 26% ~01m 07s
|++++++++++++++ | 27% ~01m 08s
|++++++++++++++ | 28% ~01m 06s
|+++++++++++++++ | 29% ~01m 05s
|+++++++++++++++ | 30% ~01m 04s
|++++++++++++++++ | 31% ~01m 03s
|++++++++++++++++ | 32% ~01m 02s
|+++++++++++++++++ | 33% ~01m 02s
|+++++++++++++++++ | 34% ~01m 01s
|++++++++++++++++++ | 35% ~60s
|++++++++++++++++++ | 36% ~59s
|+++++++++++++++++++ | 37% ~58s
|+++++++++++++++++++ | 38% ~57s
|++++++++++++++++++++ | 39% ~56s
|++++++++++++++++++++ | 40% ~55s
|+++++++++++++++++++++ | 41% ~54s
|+++++++++++++++++++++ | 42% ~53s
|++++++++++++++++++++++ | 43% ~52s
|++++++++++++++++++++++ | 44% ~51s
|+++++++++++++++++++++++ | 45% ~50s
|+++++++++++++++++++++++ | 46% ~49s
|++++++++++++++++++++++++ | 47% ~48s
|++++++++++++++++++++++++ | 48% ~47s
|+++++++++++++++++++++++++ | 49% ~46s
|+++++++++++++++++++++++++ | 50% ~46s
|++++++++++++++++++++++++++ | 51% ~45s
|+++++++++++++++++++++++++++ | 52% ~43s
|+++++++++++++++++++++++++++ | 53% ~43s
|++++++++++++++++++++++++++++ | 54% ~42s
|++++++++++++++++++++++++++++ | 55% ~41s
|+++++++++++++++++++++++++++++ | 56% ~40s
|+++++++++++++++++++++++++++++ | 57% ~39s
|++++++++++++++++++++++++++++++ | 58% ~38s
|++++++++++++++++++++++++++++++ | 59% ~37s
|+++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 61% ~35s
|++++++++++++++++++++++++++++++++ | 62% ~34s
|++++++++++++++++++++++++++++++++ | 63% ~33s
|+++++++++++++++++++++++++++++++++ | 64% ~32s
|+++++++++++++++++++++++++++++++++ | 65% ~31s
|++++++++++++++++++++++++++++++++++ | 66% ~30s
|++++++++++++++++++++++++++++++++++ | 67% ~29s
|+++++++++++++++++++++++++++++++++++ | 68% ~29s
|+++++++++++++++++++++++++++++++++++ | 69% ~28s
|++++++++++++++++++++++++++++++++++++ | 70% ~27s
|++++++++++++++++++++++++++++++++++++ | 71% ~26s
|+++++++++++++++++++++++++++++++++++++ | 72% ~25s
|+++++++++++++++++++++++++++++++++++++ | 73% ~24s
|++++++++++++++++++++++++++++++++++++++ | 74% ~23s
|++++++++++++++++++++++++++++++++++++++ | 76% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 29sDT::datatable(SMC.markers)SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
lab = rownames(SMC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "SMC markers\n(SMC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"),
plot = SMC_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)
temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Endothelial cells
Comparison between the endothelial cell communities (CD34+), and all other communities.
EC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD34+ EC I",
"CD34+ EC II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
# "CD34+ EC I",
# "CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 33s
|++ | 2 % ~01m 28s
|++ | 3 % ~01m 21s
|+++ | 4 % ~01m 16s
|+++ | 5 % ~01m 16s
|++++ | 6 % ~01m 16s
|++++ | 7 % ~01m 14s
|+++++ | 8 % ~01m 12s
|+++++ | 9 % ~01m 11s
|++++++ | 10% ~01m 11s
|++++++ | 11% ~01m 10s
|+++++++ | 12% ~01m 09s
|+++++++ | 13% ~01m 08s
|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 06s
|+++++++++ | 16% ~01m 05s
|+++++++++ | 17% ~01m 05s
|++++++++++ | 18% ~01m 04s
|++++++++++ | 19% ~01m 03s
|+++++++++++ | 20% ~01m 02s
|+++++++++++ | 21% ~01m 02s
|++++++++++++ | 22% ~01m 01s
|++++++++++++ | 23% ~60s
|+++++++++++++ | 24% ~59s
|+++++++++++++ | 25% ~58s
|++++++++++++++ | 26% ~58s
|++++++++++++++ | 27% ~57s
|+++++++++++++++ | 28% ~55s
|+++++++++++++++ | 29% ~55s
|++++++++++++++++ | 30% ~54s
|++++++++++++++++ | 31% ~53s
|+++++++++++++++++ | 32% ~52s
|+++++++++++++++++ | 33% ~52s
|++++++++++++++++++ | 34% ~51s
|++++++++++++++++++ | 35% ~50s
|+++++++++++++++++++ | 36% ~49s
|+++++++++++++++++++ | 37% ~48s
|++++++++++++++++++++ | 38% ~48s
|++++++++++++++++++++ | 39% ~47s
|+++++++++++++++++++++ | 40% ~46s
|+++++++++++++++++++++ | 41% ~45s
|++++++++++++++++++++++ | 42% ~45s
|++++++++++++++++++++++ | 43% ~44s
|+++++++++++++++++++++++ | 44% ~43s
|+++++++++++++++++++++++ | 45% ~42s
|++++++++++++++++++++++++ | 46% ~41s
|++++++++++++++++++++++++ | 47% ~41s
|+++++++++++++++++++++++++ | 48% ~40s
|+++++++++++++++++++++++++ | 49% ~39s
|++++++++++++++++++++++++++ | 51% ~38s
|++++++++++++++++++++++++++ | 52% ~37s
|+++++++++++++++++++++++++++ | 53% ~36s
|+++++++++++++++++++++++++++ | 54% ~36s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~34s
|+++++++++++++++++++++++++++++ | 57% ~33s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~30s
|+++++++++++++++++++++++++++++++ | 62% ~29s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 15sDT::datatable(EC.markers)EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
lab = rownames(EC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Endothelial cell markers\n(EC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"),
plot = EC_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)
temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)T-cells
Comparison between the T-cell communities (CD3/CD4/CD8+), and all other communities.
TC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
# "CD3+ TC I",
# "CD3+ TC II",
# "CD3+ TC III",
# "CD3+ TC IV",
# "CD3+ TC V",
# "CD3+ TC VI",
# "FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~54s
|++ | 2 % ~54s
|++ | 3 % ~54s
|+++ | 4 % ~56s
|+++ | 5 % ~54s
|++++ | 6 % ~52s
|++++ | 7 % ~50s
|+++++ | 8 % ~51s
|+++++ | 9 % ~51s
|++++++ | 10% ~50s
|++++++ | 11% ~49s
|+++++++ | 12% ~48s
|+++++++ | 13% ~47s
|++++++++ | 14% ~47s
|++++++++ | 15% ~47s
|+++++++++ | 16% ~46s
|+++++++++ | 17% ~45s
|++++++++++ | 18% ~45s
|++++++++++ | 19% ~45s
|+++++++++++ | 20% ~44s
|+++++++++++ | 21% ~44s
|++++++++++++ | 22% ~43s
|++++++++++++ | 23% ~42s
|+++++++++++++ | 24% ~41s
|+++++++++++++ | 26% ~41s
|++++++++++++++ | 27% ~40s
|++++++++++++++ | 28% ~40s
|+++++++++++++++ | 29% ~39s
|+++++++++++++++ | 30% ~38s
|++++++++++++++++ | 31% ~38s
|++++++++++++++++ | 32% ~37s
|+++++++++++++++++ | 33% ~37s
|+++++++++++++++++ | 34% ~36s
|++++++++++++++++++ | 35% ~36s
|++++++++++++++++++ | 36% ~35s
|+++++++++++++++++++ | 37% ~35s
|+++++++++++++++++++ | 38% ~34s
|++++++++++++++++++++ | 39% ~34s
|++++++++++++++++++++ | 40% ~33s
|+++++++++++++++++++++ | 41% ~32s
|+++++++++++++++++++++ | 42% ~01h 23m 58s
|++++++++++++++++++++++ | 43% ~01h 20m 36s
|++++++++++++++++++++++ | 44% ~01h 17m 23s
|+++++++++++++++++++++++ | 45% ~01h 14m 16s
|+++++++++++++++++++++++ | 46% ~01h 11m 17s
|++++++++++++++++++++++++ | 47% ~01h 08m 26s
|++++++++++++++++++++++++ | 48% ~01h 05m 43s
|+++++++++++++++++++++++++ | 49% ~01h 03m 06s
|+++++++++++++++++++++++++ | 50% ~01h 00m 35s
|++++++++++++++++++++++++++ | 51% ~58m 10s
|+++++++++++++++++++++++++++ | 52% ~55m 52s
|+++++++++++++++++++++++++++ | 53% ~53m 38s
|++++++++++++++++++++++++++++ | 54% ~51m 29s
|++++++++++++++++++++++++++++ | 55% ~49m 25s
|+++++++++++++++++++++++++++++ | 56% ~47m 26s
|+++++++++++++++++++++++++++++ | 57% ~45m 31s
|++++++++++++++++++++++++++++++ | 58% ~43m 39s
|++++++++++++++++++++++++++++++ | 59% ~41m 52s
|+++++++++++++++++++++++++++++++ | 60% ~40m 08s
|+++++++++++++++++++++++++++++++ | 61% ~38m 28s
|++++++++++++++++++++++++++++++++ | 62% ~36m 51s
|++++++++++++++++++++++++++++++++ | 63% ~35m 17s
|+++++++++++++++++++++++++++++++++ | 64% ~33m 45s
|+++++++++++++++++++++++++++++++++ | 65% ~32m 17s
|++++++++++++++++++++++++++++++++++ | 66% ~30m 52s
|++++++++++++++++++++++++++++++++++ | 67% ~29m 29s
|+++++++++++++++++++++++++++++++++++ | 68% ~28m 08s
|+++++++++++++++++++++++++++++++++++ | 69% ~26m 50s
|++++++++++++++++++++++++++++++++++++ | 70% ~25m 34s
|++++++++++++++++++++++++++++++++++++ | 71% ~24m 20s
|+++++++++++++++++++++++++++++++++++++ | 72% ~23m 08s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21m 58s
|++++++++++++++++++++++++++++++++++++++ | 74% ~20m 50s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19m 44s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18m 40s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17m 37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16m 36s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15m 37s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~14m 39s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~13m 42s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12m 47s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11m 53s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11m 01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10m 09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09m 19s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08m 30s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07m 43s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06m 56s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06m 10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05m 25s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04m 42s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03m 59s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03m 17s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02m 36s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01m 56s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01m 16s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~38s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01h 01m 04sDT::datatable(TC.markers)TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
lab = rownames(TC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "T-cell markers\n(T-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(TC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"),
plot = TC_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)
temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)B-cells
Comparison between the B-cell communities (CD79A+), and all other communities.
BC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD79+ BCplasma",
"CD79A+ BCmem"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC"
# "CD79+ BCplasma",
# "CD79A+ BCmem"
))
| | 0 % ~calculating
|+ | 1 % ~54s
|+ | 2 % ~48s
|++ | 3 % ~46s
|++ | 4 % ~44s
|+++ | 5 % ~43s
|+++ | 6 % ~45s
|++++ | 7 % ~44s
|++++ | 8 % ~43s
|+++++ | 9 % ~42s
|+++++ | 10% ~41s
|++++++ | 11% ~40s
|++++++ | 12% ~40s
|+++++++ | 13% ~39s
|+++++++ | 14% ~39s
|++++++++ | 15% ~38s
|++++++++ | 16% ~38s
|+++++++++ | 17% ~37s
|+++++++++ | 18% ~37s
|++++++++++ | 19% ~36s
|++++++++++ | 20% ~36s
|+++++++++++ | 21% ~35s
|+++++++++++ | 22% ~34s
|++++++++++++ | 23% ~42s
|++++++++++++ | 24% ~41s
|+++++++++++++ | 25% ~40s
|+++++++++++++ | 26% ~39s
|++++++++++++++ | 27% ~38s
|++++++++++++++ | 28% ~38s
|+++++++++++++++ | 29% ~37s
|+++++++++++++++ | 30% ~36s
|++++++++++++++++ | 31% ~35s
|++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
|+++++++++++++++++ | 34% ~33s
|++++++++++++++++++ | 35% ~33s
|++++++++++++++++++ | 36% ~32s
|+++++++++++++++++++ | 37% ~31s
|+++++++++++++++++++ | 38% ~31s
|++++++++++++++++++++ | 39% ~30s
|++++++++++++++++++++ | 40% ~30s
|+++++++++++++++++++++ | 41% ~29s
|+++++++++++++++++++++ | 42% ~28s
|++++++++++++++++++++++ | 43% ~28s
|++++++++++++++++++++++ | 44% ~27s
|+++++++++++++++++++++++ | 45% ~25m 17s
|+++++++++++++++++++++++ | 46% ~24m 19s
|++++++++++++++++++++++++ | 47% ~23m 23s
|++++++++++++++++++++++++ | 48% ~22m 29s
|+++++++++++++++++++++++++ | 49% ~21m 37s
|+++++++++++++++++++++++++ | 50% ~20m 47s
|++++++++++++++++++++++++++ | 51% ~19m 58s
|++++++++++++++++++++++++++ | 52% ~19m 12s
|+++++++++++++++++++++++++++ | 53% ~18m 27s
|+++++++++++++++++++++++++++ | 54% ~17m 44s
|++++++++++++++++++++++++++++ | 55% ~17m 02s
|++++++++++++++++++++++++++++ | 56% ~16m 22s
|+++++++++++++++++++++++++++++ | 57% ~15m 44s
|+++++++++++++++++++++++++++++ | 58% ~15m 06s
|++++++++++++++++++++++++++++++ | 59% ~14m 30s
|++++++++++++++++++++++++++++++ | 60% ~13m 55s
|+++++++++++++++++++++++++++++++ | 61% ~13m 21s
|+++++++++++++++++++++++++++++++ | 62% ~12m 48s
|++++++++++++++++++++++++++++++++ | 63% ~12m 16s
|++++++++++++++++++++++++++++++++ | 64% ~11m 46s
|+++++++++++++++++++++++++++++++++ | 65% ~11m 16s
|+++++++++++++++++++++++++++++++++ | 66% ~10m 47s
|++++++++++++++++++++++++++++++++++ | 67% ~10m 19s
|++++++++++++++++++++++++++++++++++ | 68% ~09m 51s
|+++++++++++++++++++++++++++++++++++ | 69% ~09m 25s
|+++++++++++++++++++++++++++++++++++ | 70% ~08m 59s
|++++++++++++++++++++++++++++++++++++ | 71% ~08m 34s
|++++++++++++++++++++++++++++++++++++ | 72% ~08m 10s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07m 46s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07m 23s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07m 00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06m 38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06m 17s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05m 56s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05m 36s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05m 16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04m 56s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04m 37s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04m 19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04m 01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03m 43s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03m 26s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03m 09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02m 53s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02m 37s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02m 21s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02m 06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01m 50s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01m 36s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01m 21s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01m 07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~53s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~39s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21m 14sDT::datatable(BC.markers)BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
lab = rownames(BC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "B-cell markers\n(B-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(BC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"),
plot = BC_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)
temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Mast cells
Comparison between the mast cell communities (KIT+), and all other communities.
MC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD68+KIT+ MC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
# "CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 12s
|+ | 2 % ~01m 38s
|++ | 3 % ~01m 31s
|++ | 4 % ~01m 24s
|+++ | 5 % ~01m 21s
|+++ | 6 % ~01m 20s
|++++ | 7 % ~01m 19s
|++++ | 8 % ~01m 16s
|+++++ | 9 % ~01m 14s
|+++++ | 10% ~01m 13s
|++++++ | 11% ~01m 13s
|++++++ | 12% ~01m 11s
|+++++++ | 13% ~01m 10s
|+++++++ | 14% ~01m 09s
|++++++++ | 15% ~01m 09s
|++++++++ | 16% ~01m 07s
|+++++++++ | 17% ~01m 06s
|+++++++++ | 18% ~01m 06s
|++++++++++ | 19% ~01m 05s
|++++++++++ | 20% ~01m 04s
|+++++++++++ | 21% ~01m 03s
|+++++++++++ | 22% ~01m 02s
|++++++++++++ | 23% ~01m 02s
|++++++++++++ | 24% ~01m 00s
|+++++++++++++ | 25% ~01m 00s
|+++++++++++++ | 26% ~59s
|++++++++++++++ | 27% ~59s
|++++++++++++++ | 28% ~57s
|+++++++++++++++ | 29% ~57s
|+++++++++++++++ | 30% ~56s
|++++++++++++++++ | 31% ~55s
|++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~53s
|+++++++++++++++++ | 34% ~53s
|++++++++++++++++++ | 35% ~52s
|++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|+++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~49s
|++++++++++++++++++++ | 40% ~48s
|+++++++++++++++++++++ | 41% ~47s
|+++++++++++++++++++++ | 42% ~46s
|++++++++++++++++++++++ | 43% ~45s
|++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~43s
|+++++++++++++++++++++++ | 46% ~43s
|++++++++++++++++++++++++ | 47% ~42s
|++++++++++++++++++++++++ | 48% ~41s
|+++++++++++++++++++++++++ | 49% ~40s
|+++++++++++++++++++++++++ | 50% ~40s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~38s
|+++++++++++++++++++++++++++ | 53% ~37s
|+++++++++++++++++++++++++++ | 54% ~36s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~35s
|+++++++++++++++++++++++++++++ | 57% ~34s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~31s
|+++++++++++++++++++++++++++++++ | 62% ~30s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~27s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~20s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 17sDT::datatable(MC.markers)MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
lab = rownames(MC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Mast cell markers\n(Mast cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(MC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"),
plot = MC_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)
temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)NK-cells
Comparison between the natural killer cell communities (NCAM1+), and all other communities.
NK.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD3+CD56+ NK I",
"CD3+CD56+ NK II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
# "CD3+CD56+ NK I",
# "CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~28s
|++ | 2 % ~26s
|++ | 3 % ~28s
|+++ | 4 % ~29s
|+++ | 5 % ~29s
|++++ | 6 % ~29s
|++++ | 7 % ~29s
|+++++ | 8 % ~28s
|+++++ | 9 % ~30s
|++++++ | 10% ~29s
|++++++ | 11% ~28s
|+++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~27s
|++++++++ | 15% ~26s
|+++++++++ | 16% ~26s
|+++++++++ | 18% ~25s
|++++++++++ | 19% ~25s
|++++++++++ | 20% ~25s
|+++++++++++ | 21% ~24s
|+++++++++++ | 22% ~24s
|++++++++++++ | 23% ~23s
|++++++++++++ | 24% ~23s
|+++++++++++++ | 25% ~23s
|+++++++++++++ | 26% ~22s
|++++++++++++++ | 27% ~22s
|++++++++++++++ | 28% ~22s
|+++++++++++++++ | 29% ~21s
|+++++++++++++++ | 30% ~21s
|++++++++++++++++ | 31% ~21s
|++++++++++++++++ | 32% ~20s
|+++++++++++++++++ | 33% ~20s
|++++++++++++++++++ | 34% ~20s
|++++++++++++++++++ | 35% ~19s
|+++++++++++++++++++ | 36% ~19s
|+++++++++++++++++++ | 37% ~19s
|++++++++++++++++++++ | 38% ~19s
|++++++++++++++++++++ | 39% ~18s
|+++++++++++++++++++++ | 40% ~18s
|+++++++++++++++++++++ | 41% ~18s
|++++++++++++++++++++++ | 42% ~17s
|++++++++++++++++++++++ | 43% ~17s
|+++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 45% ~16s
|++++++++++++++++++++++++ | 46% ~16s
|++++++++++++++++++++++++ | 47% ~16s
|+++++++++++++++++++++++++ | 48% ~15s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 53% ~14s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~13s
|++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~13s
|+++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~12s
|++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 61% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|++++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s DT::datatable(NK.markers)NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
lab = rownames(NK.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "NK markers\n(NK-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(NK.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"),
plot = NK_Volcano_TargetsA)Saving 18 x 12 in imageThe target results are given below and written to a file.
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)
temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]
DT::datatable(temp)fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
quote = FALSE,
sep = "\t",
showProgress = FALSE, verbose = FALSE)Subset scRNAseq data
List of samples to be included based on informed consent (see above).
samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx",
"sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time")
temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")
# str(scRNAseqDataCEA39@meta.data)Saving new dataset
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
"Plate", "Batch", "C.H", "Type", "percent.mt",
"nCount_SCT", "nFeature_SCT", "seurat_clusters")))
# fwrite(temp2,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp2)
#
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp)
#
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))
fwrite(temp2,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp2)
temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)
saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))Session information
Version: v1.0.1
Last update: 2022-03-19
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
* v1.0.0 Initial version.sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 grid tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-2 SeuratObject_4.0.4 Seurat_4.0.6
[4] PerformanceAnalytics_2.0.4 xts_0.12.1 zoo_1.8-9
[7] Hmisc_4.6-0 Formula_1.2-4 lattice_0.20-45
[10] survminer_0.4.9 survival_3.3-1 MASS_7.3-54
[13] ggcorrplot_0.1.3.999 GGally_2.1.2 annotables_0.1.91
[16] EnhancedVolcano_1.12.0 ggrepel_0.9.1 AnnotationFilter_1.18.0
[19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 mygene_1.30.0 org.Hs.eg.db_3.14.0
[22] DESeq2_1.34.0 SummarizedExperiment_1.24.0 MatrixGenerics_1.6.0
[25] matrixStats_0.61.0 GenomicFeatures_1.46.3 AnnotationDbi_1.56.2
[28] Biobase_2.54.0 GenomicRanges_1.46.1 GenomeInfoDb_1.30.0
[31] IRanges_2.28.0 S4Vectors_0.32.3 BiocGenerics_0.40.0
[34] patchwork_1.1.0.9000 labelled_2.9.0 openxlsx_4.2.5
[37] sjPlot_2.8.10 UpSetR_1.4.0 ggpubr_0.4.0
[40] forestplot_2.0.1 checkmate_2.0.0 magrittr_2.0.2
[43] pheatmap_1.0.12 devtools_2.4.3 usethis_2.1.5
[46] BlandAltmanLeh_0.3.1 tableone_0.13.0 haven_2.4.3
[49] eeptools_1.2.4 DT_0.20 knitr_1.37
[52] forcats_0.5.1 stringr_1.4.0 purrr_0.3.4
[55] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[58] data.table_1.14.2 naniar_0.6.1 tidyr_1.1.4
[61] dplyr_1.0.7 optparse_1.7.1 readr_2.1.1
[64] pander_0.6.4 rmarkdown_2.11 worcs_0.1.9.1
loaded via a namespace (and not attached):
[1] mitools_2.4 pbapply_1.5-0 vctrs_0.3.8 mgcv_1.8-39 blob_1.2.2 spatstat.data_2.1-2
[7] later_1.3.0 nloptr_1.2.2.3 DBI_1.1.2 uwot_0.1.11 rappdirs_0.3.3 gsubfn_0.7
[13] jpeg_0.1-9 zlibbioc_1.40.0 sjmisc_2.8.9 htmlwidgets_1.5.4 mvtnorm_1.1-3 future_1.23.0
[19] leiden_0.3.9 parallel_4.1.2 irlba_2.3.5 markdown_1.1 Rcpp_1.0.8.3 KernSmooth_2.23-20
[25] promises_1.2.0.1 limma_3.50.0 DelayedArray_0.20.0 ggeffects_1.1.1 pkgload_1.2.4 fs_1.5.2
[31] textshaping_0.3.6 ranger_0.13.1 digest_0.6.29 png_0.1-7 sctransform_0.3.2 cowplot_1.1.1
[37] pkgconfig_2.0.3 ggbeeswarm_0.6.0 estimability_1.3 minqa_1.2.4 reticulate_1.22 beeswarm_0.4.0
[43] xfun_0.29 bslib_0.3.1 tidyselect_1.1.1 performance_0.8.0 reshape2_1.4.4 ica_1.0-2
[49] viridisLite_0.4.0 rtracklayer_1.54.0 pkgbuild_1.3.1 rlang_1.0.2 jquerylib_0.1.4 glue_1.6.2
[55] ensembldb_2.18.2 modelr_0.1.8 emmeans_1.7.2 ggsignif_0.6.3 bayestestR_0.11.5 labeling_0.4.2
[61] maptools_1.1-2 httpuv_1.6.5 class_7.3-20 Rttf2pt1_1.3.9 TH.data_1.1-0 annotate_1.72.0
[67] jsonlite_1.7.2 XVector_0.34.0 bit_4.0.4 mime_0.12 systemfonts_1.0.3 gridExtra_2.3
[73] Rsamtools_2.10.0 stringi_1.7.6 insight_0.16.0 processx_3.5.2 spatstat.sparse_2.1-0 scattermore_0.7
[79] survey_4.1-1 quadprog_1.5-8 bitops_1.0-7 cli_3.2.0 sqldf_0.4-11 maps_3.4.0
[85] RSQLite_2.2.9 prereg_0.5.0 rticles_0.22 rsconnect_0.8.25 rstudioapi_0.13 GenomicAlignments_1.30.0
[91] nlme_3.1-155 locfit_1.5-9.4 listenv_0.8.0 miniUI_0.1.1.1 survMisc_0.5.5 dbplyr_2.1.1
[97] sessioninfo_1.2.2 readxl_1.3.1 lifecycle_1.0.1 munsell_0.5.0 cellranger_1.1.0 ggsci_2.9
[103] codetools_0.2-18 coda_0.19-4 vipor_0.4.5 lmtest_0.9-39 sys_3.4 htmlTable_2.4.0
[109] proto_1.0.0 xtable_1.8-4 ROCR_1.0-11 abind_1.4-5 farver_2.1.0 parallelly_1.30.0
[115] km.ci_0.5-2 credentials_1.3.2 RANN_2.6.1 askpass_1.1 visdat_0.5.3 BiocIO_1.4.0
[121] sjstats_0.18.1 goftest_1.2-3 RcppAnnoy_0.0.19 cluster_2.1.2 future.apply_1.8.1 extrafontdb_1.0
[127] Matrix_1.4-0 ellipsis_0.3.2 prettyunits_1.1.1 lubridate_1.8.0 ggridges_0.5.3 reprex_2.0.1
[133] igraph_1.2.11 sjlabelled_1.1.8 remotes_2.4.2 parameters_0.17.0 spatstat.utils_2.3-0 testthat_3.1.1
[139] getopt_1.20.3 htmltools_0.5.2 BiocFileCache_2.2.0 yaml_2.2.1 utf8_1.2.2 plotly_4.10.0
[145] XML_3.99-0.8 e1071_1.7-9 foreign_0.8-82 withr_2.5.0 fitdistrplus_1.1-6 BiocParallel_1.28.3
[151] bit64_4.0.5 effectsize_0.6.0.1 multcomp_1.4-18 ProtGenerics_1.26.0 spatstat.core_2.3-2 Biostrings_2.62.0
[157] ragg_1.2.1 memoise_2.0.1 evaluate_0.14 geneplotter_1.72.0 tzdb_0.2.0 extrafont_0.17
[163] callr_3.7.0 ps_1.6.0 curl_4.3.2 fansi_1.0.2 tensor_1.5 cachem_1.0.6
[169] desc_1.4.0 deldir_1.0-6 proj4_1.0-10.1 rjson_0.2.21 rstatix_0.7.0 rprojroot_2.0.2
[175] sass_0.4.0 sandwich_3.0-1 RCurl_1.98-1.5 proxy_0.4-26 car_3.0-12 xml2_1.3.3
[181] httr_1.4.2 assertthat_0.2.1 boot_1.3-28 globals_0.14.0 R6_2.5.1 nnet_7.3-17
[187] progress_1.2.2 genefilter_1.76.0 KEGGREST_1.34.0 ggrastr_1.0.1 splines_4.1.2 carData_3.0-5
[193] colorspace_2.0-3 generics_0.1.1 base64enc_0.1-3 chron_2.3-56 gridtext_0.1.4 gert_1.5.0
[199] pillar_1.7.0 ggalt_0.4.0 sp_1.4-6 GenomeInfoDbData_1.2.7 plyr_1.8.6 gtable_0.3.0
[205] rvest_1.0.2 zip_2.2.0 restfulr_0.0.13 latticeExtra_0.6-29 biomaRt_2.50.2 fastmap_1.1.0
[211] Cairo_1.5-14 crosstalk_1.2.0 datawizard_0.3.0 vcd_1.4-9 broom_0.7.11 openssl_1.4.6
[217] scales_1.1.1 arm_1.12-2 filelock_1.0.2 backports_1.4.1 lme4_1.1-27.1 hms_1.1.1
[223] Rtsne_0.15 shiny_1.7.1 KMsurv_0.1-5 ash_1.0-15 polyclip_1.10-0 lazyeval_0.2.2
[229] crayon_1.5.0 reshape_0.8.8 rpart_4.1.16 spatstat.geom_2.3-1 compiler_4.1.2 ggtext_0.1.1 Saving environment
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))| © 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com swvanderlaan.github.io. |
---
title: "Mapping targets to single cells in plaques."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: Accompanying 'Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.'
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---

# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan"

### MacBook Air 
# ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")


setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r}
ggplot2::theme_set(ggplot2::theme_minimal())
pander::panderOptions("table.split.table", Inf)
```

```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

# for Seurat etc
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")
```

```{r}

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
# install.packages("Seurat")
install.packages.auto("Seurat") # latest version
library("Seurat")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and
RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.

# Background

Here we map the `r TRAIT_OF_INTEREST` to single-cells from the plaques.

```{r targets for mapping}

library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes

```

# Load data

First we will load the data:

-   scRNAseq experimental data and rename the cell types.
-   Athero-Express clinical data.

Here we load the latest dataset from our Athero-Express single-cell RNA experiment.

```{r LoadData}

# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
# 
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData

```

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as 'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as 'communities' of cells that exhibit similar properties, *i.e.* similar defining markers (*e.g. KIT*).

We will rename the cell types to human readable names.

```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

```

```{r}
unique(scRNAseqData@active.ident)
```

```{r}
celltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono", 
               "CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes", 
               "CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
               "CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
               "CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
               
               # T-cells
               "CD3+ T Cells I" = "CD3+ TC I",
               "CD3+ T Cells II" = "CD3+ TC II", 
               "CD3+ T Cells III" = "CD3+ TC III", 
               "CD3+ T Cells IV" = "CD3+ TC IV", 
               "CD3+ T Cells V" = "CD3+ TC V", 
               "CD3+ T Cells VI" = "CD3+ TC VI", 
               "FOXP3+ T Cells" = "FOXP3+ TC",
               
               # Endothelial cells
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               
               # SMC
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               
               # NK Cells
               "CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
               "CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
               # Mast
               "CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
               
               "CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem", 
               "CD79+ Plasma B Cells" = "CD79+ BCplasma")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
```

```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

```

## Clinical data

Loading the Athero-Express clinical data.

```{r LoadAEDB}

AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220319.",TRAIT_OF_INTEREST,".AEDB.CEA.RDS"))

```


```{r }

# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")

basetable_bin = c("Gender",  "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

## AESCRNA: baseline characteristics

### Preparation

```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)

# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
```

```{r }
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                                 (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                                   informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                                   informedconsent != "no, died" &
                                   informedconsent != "yes, no tissue, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no health treatment" &
                                   informedconsent != "yes, no tissue, no questionnaires" &
                                   informedconsent != "yes, no tissue, health treatment when possible" &
                                   informedconsent != "yes, no tissue" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                                   informedconsent != "no, doesn't want to" &
                                   informedconsent != "no, unable to sign" &
                                   informedconsent != "no, no reaction" &
                                   informedconsent != "no, lost" &
                                   informedconsent != "no, too old" &
                                   informedconsent != "yes, no medical info, health treatment when possible" & 
                                   informedconsent != "no (never asked for IC because there was no tissue)" &
                                   informedconsent != "no, endpoint" &
                                   informedconsent != "nooit geincludeerd" & 
                                   informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
                                   informedconsent != "yes, no tissue, no commerical business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" & 
                                   informedconsent != "yes, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no commerical business" & 
                                   informedconsent != "yes, health treatment when possible, no commercial business" & 
                                   informedconsent != "yes, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" & 
                                   informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)

```

### Baseline

Showing the baseline table for the scRNAseq data in 39 CEA patients with
informed consent.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format.

```{r }
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_commercial.BaselineTable.xlsx"), 
#            format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , 
#            rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.32pts.after_qc.IC_academic.BaselineTable.xlsx"), 
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

```

# AESCRNA

## Quality control

Here review the number of cells per sample, plate, and patients. And plot the
ratio's per sample and study number.

```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))

# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)

cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqData@meta.data$Patient))

cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())


# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 1,
#         col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()

# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 2,
#         col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()



```

## Visualisations

Let's project known cellular markers.

```{r Visualisation: tSNE Exploration}

UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))

# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))

# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))

# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))

# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))

# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

```

## Targets of interest:

We check whether the targets genes were sequenced using our method.

```{r list target genes}
length(target_genes)
target_genes

```

### Expression in cell communities

```{r Visualisation: preparation}

# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "RP11-145E5.5",
#                      "ZNF32", "BCAM", "DUPD1", "PVRL2")
# 
# temp = target_genes[!target_genes %in% target_genes_rm]
# 
# target_genes_qc <- c(temp, "DUSP27", "NECTIN2")

target_genes_qc <- target_genes
target_genes_qc
```

```{r Visualisation: Targets Feature and Dot Plots, message=FALSE, warning=FALSE}
library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())

rm(p1)

# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
#             cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             combine = TRUE)
# 
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())


```

```{r Visualisation: Targets}
# VlnPlot(scRNAseqData, features = "DUSP27")

# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")), 
       dir.create(file.path(PLOT_loc, "/VlnPlot")), 
       FALSE)
VlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqData, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
  
  # print(vp1)
  
}

```

### Differential expression between cell communities

Here we project genes to only the broad cell communities:

-   macrophages
-   endothelial cells
-   smooth muscle cells
-   T-cells
-   B-cells
-   Mast cells
-   NK-cells
-   Mixed cells

#### Macrophages

```{r}
unique(scRNAseqData@active.ident)
```

Comparison between the macrophages cell communities (*CD14/CD68*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano MAC calculate}

MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC"), 
                          ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf", 
                                      #"CD68+CD1C+ DC", 
                                      #"CD68+CD4+ Mono",
                                      #"CD68+IL18+TLR4+TREM2+ MRes",
                                      #"CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MAC.markers)
```

```{r Visualisation: Volcano MAC, message=FALSE, warning=FALSE}
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MAC}
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MAC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Smooth muscle cells

Comparison between the smooth muscle cell communities (*ACTA2*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano SMC calculate}

SMC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      #"ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(SMC.markers)
```

```{r Visualisation: Volcano SMC, message=FALSE, warning=FALSE}
SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results SMC}
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results SMC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Endothelial cells

Comparison between the endothelial cell communities (*CD34*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano EC calculate}

EC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD34+ EC I", 
                                      "CD34+ EC II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(EC.markers)
```

```{r Visualisation: Volcano EC, message=FALSE, warning=FALSE}
EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results EC}
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results EC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### T-cells

Comparison between the T-cell communities (*CD3/CD4/CD8*<sup>+</sup>), and all
other communities.

```{r Visualisation: Volcano Tcell calculate}

TC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      # "CD3+ TC I",
                                      # "CD3+ TC II", 
                                      # "CD3+ TC III", 
                                      # "CD3+ TC IV", 
                                      # "CD3+ TC V", 
                                      # "CD3+ TC VI", 
                                      # "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(TC.markers)
```

```{r Visualisation: Volcano Tcell, message=FALSE, warning=FALSE}
TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(TC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results TC}
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results TC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### B-cells

Comparison between the B-cell communities (*CD79A*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Bcell calculate}

BC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD79+ BCplasma", 
                                      "CD79A+ BCmem"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC"
                                      # "CD79+ BCplasma", 
                                      # "CD79A+ BCmem"
                                      ))

DT::datatable(BC.markers)
```

```{r Visualisation: Volcano Bcell, message=FALSE, warning=FALSE}
BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(BC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results BC}
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results BC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Mast cells

Comparison between the mast cell communities (*KIT*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Mast calculate}

MC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+KIT+ MC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      # "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MC.markers)
```

```{r Visualisation: Volcano Mast, message=FALSE, warning=FALSE}
MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(MC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MC}
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### NK-cells

Comparison between the natural killer cell communities (*NCAM1*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano NK calculate}

NK.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      # "CD3+CD56+ NK I",
                                      # "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(NK.markers)
```

```{r Visualisation: Volcano NK, message=FALSE, warning=FALSE}
NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(NK.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results NK}
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results NK: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

# Subset scRNAseq data

List of samples to be included based on informed consent (see above).

```{r}
samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)

```

```{r}
scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)
```

```{r}
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
                           "Age", "Gender",
                           "TC_final", "LDL_final", "HDL_final", "TG_final",
                           "systolic", "diastoli", "GFR_MDRD", "BMI",
                           "KDOQI", "BMI_WHO",
                           "SmokerStatus", "AlcoholUse",
                           "DiabetesStatus",
                           "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
                           "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
                           "Stroke_Dx",
                           "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                           "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                           "restenos", "stenose",
                           "CAD_history", "PAOD", "Peripheral.interv",
                           "EP_composite", "EP_composite_time", "EP_major", "EP_major_time")

temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)

```

```{r}
scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")

# str(scRNAseqDataCEA39@meta.data)

```

## Saving new dataset

```{r}
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
                                                                 "Plate", "Batch", "C.H", "Type", "percent.mt",
                                                                 "nCount_SCT", "nFeature_SCT", "seurat_clusters")))

# fwrite(temp2,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp2)
# 
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))

fwrite(temp2,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp2)

temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.1
    Last update:  2022-03-19
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
    * v1.0.0 Initial version.

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))

```

+---------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
+---------------------------------------------------------------------------------------------------------------------------------------+
